CTdata.org provides these data sets in 1, 3, and 5 year averages due some of the levels showing little data at the one year level. Therefore, depending on which combination of characteristics the user is interested in, data may not be available at the 1 or 3 level data, instead only available at the 5 year level.
But how can the user know at which level should they start exploring?
That’s where these tables come in.
For example, are you looking for how many births took place in Hartford to mother’s age 45 years and older and that carried to full term?
You’ll have to first look that the 3-year aggregation data set for that information, becasue no data are available in the 1-year data set. These are the types of questions that can be answered with these tables.
Please note that these tables reflect availabilities for the latest years available. If no data are available for a given combination of characteristics, the Available column will be blank.
demo_1 <- read.csv(paste0(path_to_data, "/", "maternal_characteristics_demographics_1-Year.csv"), stringsAsFactors = F, header=T)
demo_3 <- read.csv(paste0(path_to_data, "/", "maternal_characteristics_demographics_3-Year.csv"), stringsAsFactors = F, header=T)
demo_5 <- read.csv(paste0(path_to_data, "/", "maternal_characteristics_demographics_5-Year.csv"), stringsAsFactors = F, header=T)
demo_all <- rbind(demo_1, demo_3, demo_5)
years <- c("2014", "2012-2014", "2010-2014")
demo_select <- demo_all[demo_all$Measure.Type == "Number" & demo_all$Year %in% years,]
demo_select$Year <- factor(demo_select$Year, levels = c("2014", "2012-2014", "2010-2014"))
demo_select2 <- demo_select %>%
select(-c(Measure.Type, FIPS)) %>%
arrange(Town, Birth.Weight, Gestational.Age, Mother.s.Age, Mother.s.Race.Ethnicity) %>%
group_by(Town, Birth.Weight, Gestational.Age, Mother.s.Age, Mother.s.Race.Ethnicity, Year) %>%
mutate(value_avail = ifelse((Value > 0), 1, 0))
demo_select3 <- demo_select2 %>%
group_by(`Town`, Birth.Weight, Gestational.Age, Mother.s.Age, Mother.s.Race.Ethnicity) %>%
mutate(max_value_avail = max(value_avail))
demo_select4 <- demo_select3 %>%
mutate(year_avail = ifelse((value_avail == 0 & max_value_avail == 0), "None", NA),
year_avail = ifelse((value_avail == 0 & max_value_avail == 1), NA, NA),
year_avail = ifelse((value_avail == 1 & max_value_avail == 1), Year, NA))
demo_select4[is.na(demo_select4)] <- 4
demo_select5 <- demo_select4 %>%
group_by(`Town`, Birth.Weight, Gestational.Age, Mother.s.Age, Mother.s.Race.Ethnicity) %>%
mutate(min_year = min(year_avail))
demo_select5 <- as.data.frame(demo_select5)
demo_select6 <- demo_select5 %>%
mutate(Earliest.Data.Available = ifelse(min_year == 1, "2014",
ifelse(min_year== 2, "2012-2014",
ifelse(min_year == 3, "2010-2014",
ifelse(min_year == 4, NA, NA)))))
demo_available <- demo_select6 %>%
select(-c(year_avail, Year, Variable, Value, value_avail, max_value_avail, year_avail, min_year))
demo_available <- demo_available[!duplicated(demo_available), ]
table <- head(demo_select6_print)
knitr::kable(table)
| Town | Year | Birth.Weight | Gestational.Age | Mother.s.Age | Mother.s.Race.Ethnicity | Variable | Value | value_avail | max_value_avail | year_avail | min_year | Earliest.Data.Available |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Andover | 2014 | All | 37 weeks or more | 0 to 14 years | All | Births | 0 | 0 | 0 | 4 | 4 | NA |
| Andover | 2012-2014 | All | 37 weeks or more | 0 to 14 years | All | Births | 0 | 0 | 0 | 4 | 4 | NA |
| Andover | 2010-2014 | All | 37 weeks or more | 0 to 14 years | All | Births | 0 | 0 | 0 | 4 | 4 | NA |
| Andover | 2014 | All | 37 weeks or more | 15 to 19 years | All | Births | 0 | 0 | 1 | 4 | 3 | 2010-2014 |
| Andover | 2012-2014 | All | 37 weeks or more | 15 to 19 years | All | Births | 0 | 0 | 1 | 4 | 3 | 2010-2014 |
| Andover | 2010-2014 | All | 37 weeks or more | 15 to 19 years | All | Births | 1 | 1 | 1 | 3 | 3 | 2010-2014 |
The overall annual trend dating back to 1999, shows that the total number of births occuring in CT has declined from just over 86,000 in 1999 to just over 72,000 in 2014.
When we break down those totals into age groups, we see a decline in teen births, and an increase in mother’s aged 45+.
When we break down those totals into education levels, we see an increase over time in the number of mother’s who have received 17 years or more of education, while mother’s with high school, GED, or college level education have all decreased. When we break down those totals into groups indentified by when the mother’s first intiated prenatal care, we see the majority of mother’s inititating prenatal care within the first trimester. We’ve also broken out the total number of mother’s considered in the “Late” category. Late prenatal care is the initiation of prenatal care after the first trimester of pregnancy.